Contrastive Model Training for Audio-Video Music Recommendation
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Solution Overview
Problem
The recommendation of background music for videos based on popularity leads to a mismatch between the music and the video content, resulting in a low hit rate of the recommended music being used in user-generated content.
Innovation Solution
A model training method involving constructing sample pairs of video and audio samples, extracting feature information using video and audio feature extraction models, and adjusting model parameters based on a contrastive loss function to improve matching between video and audio content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If background music is recommended based on popularity, then the quantity of recommended music is large and easy to obtain, but the matching accuracy between music and video content deteriorates
Solution Approach 1:
The patent transforms the recommendation approach from popularity-based parameters to feature-matching parameters. By extracting and comparing feature vectors from video and audio data, the system changes the fundamental parameters used for recommendation, enabling accurate matching while maintaining efficiency through automated feature extraction and contrastive learning.
Solution Approach 2:
The patent replaces the mechanical/popularity-based recommendation system with an intelligent feature-matching system. Instead of relying on simple counting and ranking mechanisms, the system uses deep learning models to extract semantic features and compute matching degrees, substituting mechanical operations with intelligent processing.
2Ease of operation
If background music is recommended based on popularity, then the system is simple to operate, but the hit rate of recommended music deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting features from video and audio data, computing matching degrees, and generating recommendations without manual intervention. The contrastive learning model self-optimizes through automated training on positive and negative sample pairs, eliminating the need for manual curation while maintaining high hit rates.
Solution Approach 2:
The patent implements feedback mechanisms through contrastive loss computation during model training. The system continuously learns from positive sample pairs (matching video-audio) and negative sample pairs (non-matching), adjusting model parameters to improve recommendation accuracy and hit rate over time.
3Measurement precision
If feature extraction models are trained with contrastive learning, then the matching accuracy improves, but the model training complexity increases
Solution Approach 1:
The patent segments the training process into distinct components: positive sample pair construction, negative sample pair construction, feature extraction, contrastive loss computation, and parameter optimization. This segmentation allows each component to be independently optimized and managed, reducing overall training complexity while maintaining high matching accuracy.
Solution Approach 2:
The patent performs preliminary actions by pre-processing video and audio data into standardized feature vectors before training. Positive and negative sample pairs are pre-constructed and organized, allowing the main training process to focus on learning optimal matching parameters rather than dealing with raw data complexity.
Data Source
AI summary
A model training method includes: constructing a set of sample pairs based on at least one video sample and at least one audio sample, the set of sample pairs including at least one positive sample pair including a video sample and an audio sample that have a matching relationship and at least one negative sample pair including a video sample and an audio sample that do not have a matching relationship; extracting video feature information corresponding to the video sample through a video feature extraction model, and extracting audio feature information corresponding to the audio sample through an audio feature extraction model; determining a value of a contrastive loss function based on the video feature information and the audio feature information in the same sample pair; and adjusting parameters of the video feature extraction model and the audio feature extraction model based on the value of the contrastive loss function.